How many launches showed up, how many passed the filters, and what type of setup appeared.
Student notebook for on-chain markets
Learning Solana launches in public.
I am building a small system that watches new token launches, filters obvious risk, and tracks what happens after. The goal is not to call coins. The goal is to learn from the data.
Today
signals
mistakes
changes
Latest result
Day 1: bad result, useful data.
First real session ran for 10.5 hours. The detector found movement, but execution friction ate the edge: small trade size, slippage, and delayed exits.
Runtime
10.5h
Trades
48
Closed
44
Net
-0.249 SOL
Fixes shipped
- Sell slippage widened from 3% to 15%
- Price checks moved from polling to WebSocket triggers
- AI veto prompt cleaned up for serial-launcher patterns
Early
learning phase
Daily
results posted
Public
mistakes included
No calls
notes only
Daily note format
Simple enough to post every day.
The best outcome, the worst outcome, and the part of the move that looked obvious only after the fact.
One small adjustment to the system, the process, or the way I read the data tomorrow.
The system
A small detector, a daily notebook, and a lot of testing.
Detect
Watch new Solana token launches and keep the useful ones in view.
Filter
Remove obvious risk before pretending a chart means anything.
Track
Follow post-launch behavior and compare the signal to the outcome.
Learn
Post the results, keep the mistakes visible, and improve the next test.
Principles
Building in public without pretending to know everything.
- No live buy calls
- No paid group
- No guaranteed results
- Feedback welcome